Non-Communicable Disease Burden (NCD)

# Load necessary packages
pacman::p_load(tidyverse, knitr, here, plotly)

# Import the non-communicable diseases data
data_ncd <- read_csv(here("data/burden-of-disease-ncd.csv"))

## Rows: 8010 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, DALYs (Disability-Adjusted Life Years) - Non-communicable dis...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Filter the dataset to include only the three countries chosen by your group.
data_ncd_mini <- data_ncd %>%
  filter(Entity %in% c("Botswana", "Brazil", "Brunei"))

# convert column names to lower cases

names(data_ncd_mini) <- tolower(make.names(names(data_ncd_mini)))

#rename daly
data_ncd_mini <- data_ncd_mini %>%
  rename(
    daly = dalys..disability.adjusted.life.years....non.communicable.diseases...sex..both...age..age.standardized..rate.
  )

# rename entity to country
data_ncd_mini <- data_ncd_mini %>%
  rename(
    country = entity
  )

Table of Estimates for NCD Burden Over Time

# Here render a table for the DALY burden over time for the three countries. 
# You should pivot the data wider to show each country in a separate column.

# Pivot to wide format, keeping country and code as identifiers
data_ncd_wide <- data_ncd_mini %>%
  pivot_wider(
    id_cols    = c(country, code),
    names_from = year,
    values_from= daly
  )

# Render the wide table using kable()
knitr::kable(
  data_ncd_wide,
  caption = "DALYs by Country and Year",
  digits = 2,
  align = c("l", "l", rep("r", ncol(data_ncd_wide) - 2))
)

DALYs by Country and Year

country	code	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Botswana	BWA	26190.62	26509.04	27015.22	27528.12	28218.32	28895.38	29361.37	29736.86	30315.12	30743.29	31281.86	31559.43	31665.12	31268.71	30380.28	29328.62	28672.05	27904.02	27746.63	27480.15	27403.35	27281.79	27011.70	26801.49	26616.81	26407.15	26063.55	25947.89	25835.51	25671.73
Brazil	BRA	26878.18	26274.16	25980.82	25987.45	25642.50	25320.93	25135.56	24790.31	24678.36	24505.38	24259.14	24054.18	23876.85	23726.83	23577.01	23171.79	22954.68	22684.44	22432.92	22202.47	22005.03	21845.06	21549.33	21327.61	21068.12	20914.63	20920.49	20629.32	20465.81	20309.17
Brunei	BRN	29769.74	29192.58	28694.22	28181.41	27201.24	26569.97	25848.91	25972.17	25911.14	25767.12	25561.99	25067.34	24968.82	25113.33	25023.13	24977.85	24710.75	24177.09	23641.58	23597.07	23555.27	23408.48	23299.45	23390.06	23337.90	23250.59	23110.63	22952.47	22834.64	22663.02

# Use kable() from the knitr package to render the table.

Plot Showing Trends in NCD Burden Over Time

# Here, plot a line graph showing the trend of DALY burden for the chosen countries over time. You can use ggplot2 to create this plot. Each country's line should be a different color.
# NOTE: Use your long 'mini' data frame for plotting, not the wide data used for the table. 
data_ncd_linechart <-data_ncd_mini %>%
  ggplot(aes(x = year, y = daly, color = country)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  labs(
    title    = "DALYs Over Time",
    x        = "Year",
    y        = "Disability-Adjusted Life Years (DALY)",
    color    = "Country"
  ) +
  theme_minimal()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

ggplotly(data_ncd_linechart)

Summary of NCD Burden Findings

Provide a brief analysis based on the data presented in the table and chart. Highlight any significant findings or patterns. About 3 sentences.

Between 1990 and 2019, all three countries saw overall declines in non-communicable disease DALYs, with Brazil showing the most consistent year-on-year reduction from about 26 800 to just over 20 000.

Brunei’s DALYs fell from roughly 29 500 to 22 700, though there was a mid-2000s plateau before renewed improvement.

In contrast, Botswana experienced a pronounced rise in DALYs—peaking near 32 000 in the mid-2000s—before reversing course to end at approximately 25 600, mirroring regional gains in disease control but highlighting a later start.